Activity-based travel demand models are generally considered superior to their trip-based counterparts, as activity-based models (ABMs) explicitly model individuals in contrast to the aggregate nature of trip-based models. There have been a number of comparisons between trip- and activity-based models, but these comparisons focus almost exclusively on the technical ability of the two model types, while not considering the practical benefits an ABM may or may not have to a transportation agency. This research performs a more holistic comparison between trip- and activity-based models, focused specifically on the practical differences between model types, both in terms of usability and capability for complex analysis. We use the existing Wasatch Front model as a representative trip-based model, and an ActivitySim implementation in the same area as a representative ABM. We create three hypothetical scenarios in both models: a change in land use, an improvement to commuter rail service, and an increase in remote work. We discuss the process of creating each scenario in both models, and perform several example analyses with each scenario and model. We find that many commonly-cited reasons for the lack of ABM adoption may not be as applicable as previously thought. ABMs are often considered more complicated than trip-based models, requiring more data and computational resources. While ABMs do require more input data, we found that in our case the complexity of the model and the computational resources required were similar between model types. Additionally, the ABM allows for much more intuitive and straightforward interpretation of results.

The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models -- which have detailed understanding of the person making a trip and its purpose -- and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models.